R. Krishnasamy

526 total citations
17 papers, 433 citations indexed

About

R. Krishnasamy is a scholar working on Computer Networks and Communications, Control and Systems Engineering and Statistical and Nonlinear Physics. According to data from OpenAlex, R. Krishnasamy has authored 17 papers receiving a total of 433 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Networks and Communications, 10 papers in Control and Systems Engineering and 6 papers in Statistical and Nonlinear Physics. Recurrent topics in R. Krishnasamy's work include Neural Networks Stability and Synchronization (14 papers), Stability and Control of Uncertain Systems (10 papers) and Neural Networks and Applications (4 papers). R. Krishnasamy is often cited by papers focused on Neural Networks Stability and Synchronization (14 papers), Stability and Control of Uncertain Systems (10 papers) and Neural Networks and Applications (4 papers). R. Krishnasamy collaborates with scholars based in India. R. Krishnasamy's co-authors include P. Balasubramaniam, R. Rakkiyappan, A. Chandrasekar, Raju K. George, S. Lakshmanan, Prakash Mani and A‎. ‎Vinodkumar and has published in prestigious journals such as Neurocomputing, Chaos Solitons & Fractals and Applied Mathematical Modelling.

In The Last Decade

R. Krishnasamy

16 papers receiving 425 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
R. Krishnasamy India 10 340 253 114 90 70 17 433
Dongxue Peng China 10 429 1.3× 205 0.8× 218 1.9× 39 0.4× 70 1.0× 12 565
Zengyun Wang China 13 325 1.0× 139 0.5× 158 1.4× 42 0.5× 102 1.5× 29 443
S. Saravanan India 14 418 1.2× 218 0.9× 131 1.1× 46 0.5× 141 2.0× 40 512
Yunzhe Men China 6 360 1.1× 452 1.8× 67 0.6× 78 0.9× 59 0.8× 10 559
J. Yogambigai India 13 350 1.0× 117 0.5× 132 1.2× 25 0.3× 51 0.7× 17 375
Jingtao Man China 15 502 1.5× 224 0.9× 190 1.7× 36 0.4× 139 2.0× 30 620
Tingting Ru China 6 261 0.8× 232 0.9× 54 0.5× 29 0.3× 58 0.8× 11 360
Mei-Lan Tang China 11 287 0.8× 205 0.8× 107 0.9× 60 0.7× 93 1.3× 29 396
Mingang Hua China 13 261 0.8× 242 1.0× 82 0.7× 27 0.3× 81 1.2× 34 372
El Abed Assali Tunisia 11 549 1.6× 104 0.4× 268 2.4× 35 0.4× 184 2.6× 23 606

Countries citing papers authored by R. Krishnasamy

Since Specialization
Citations

This map shows the geographic impact of R. Krishnasamy's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by R. Krishnasamy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites R. Krishnasamy more than expected).

Fields of papers citing papers by R. Krishnasamy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by R. Krishnasamy. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by R. Krishnasamy. The network helps show where R. Krishnasamy may publish in the future.

Co-authorship network of co-authors of R. Krishnasamy

This figure shows the co-authorship network connecting the top 25 collaborators of R. Krishnasamy. A scholar is included among the top collaborators of R. Krishnasamy based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with R. Krishnasamy. R. Krishnasamy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Krishnasamy, R., et al.. (2023). Non-fragile sampled-data control for synchronization of chaotic fractional-order delayed neural networks via LMI approach. Chaos Solitons & Fractals. 169. 113252–113252. 19 indexed citations
2.
Krishnasamy, R., et al.. (2023). Robust stability of uncertain stochastic switched inertial neural networks with time‐varying delay using state‐dependent switching law. Mathematical Methods in the Applied Sciences. 46(12). 13155–13175. 5 indexed citations
3.
4.
Krishnasamy, R. & Raju K. George. (2021). Mean-square asymptotic stability of stochastic inertial neural networks with time-delay and Markovian jump parameters. International Journal of Dynamical Systems and Differential Equations. 11(5/6). 527–527. 2 indexed citations
5.
Krishnasamy, R. & Raju K. George. (2021). Mean-square asymptotic stability of stochastic inertial neural networks with time-delay and Markovian jump parameters. International Journal of Dynamical Systems and Differential Equations. 11(5/6). 527–527.
6.
Krishnasamy, R. & Raju K. George. (2018). Stochastic stability of mode-dependent Markovian jump inertial neural networks. The Journal of Analysis. 27(1). 179–196. 7 indexed citations
7.
Rakkiyappan, R., et al.. (2016). Synchronization and periodicity of coupled inertial memristive neural networks with supremums. Neurocomputing. 214. 739–749. 75 indexed citations
8.
Krishnasamy, R. & P. Balasubramaniam. (2015). Robust stability results for nonlinearMarkovian jump systems with mode‐dependent time‐varying delays and randomly occurring uncertainties. Complexity. 21(6). 50–60. 8 indexed citations
9.
Krishnasamy, R. & P. Balasubramaniam. (2014). A descriptor system approach to the delay-dependent exponential stability analysis for switched neutral systems with nonlinear perturbations. Nonlinear Analysis Hybrid Systems. 15. 23–36. 36 indexed citations
10.
Balasubramaniam, P. & R. Krishnasamy. (2014). Robust Exponential Stabilization Results for Impulsive Neutral Time-Delay Systems with Sector-Bounded Nonlinearity. Circuits Systems and Signal Processing. 33(9). 2741–2759. 13 indexed citations
11.
Krishnasamy, R. & P. Balasubramaniam. (2014). Stochastic Stability Analysis for Switched Genetic Regulatory Networks with Interval Time-Varying Delays Based on Average Dwell Time Approach. Stochastic Analysis and Applications. 32(6). 1046–1066. 8 indexed citations
12.
Krishnasamy, R. & P. Balasubramaniam. (2013). Stabilisation analysis for switched neutral systems based on sampled-data control. International Journal of Systems Science. 46(14). 2531–2546. 14 indexed citations
13.
Balasubramaniam, P., R. Krishnasamy, & R. Rakkiyappan. (2012). Delay-dependent stability criterion for a class of non-linear singular Markovian jump systems with mode-dependent interval time-varying delays. Communications in Nonlinear Science and Numerical Simulation. 17(9). 3612–3627. 57 indexed citations
14.
Balasubramaniam, P., R. Krishnasamy, & R. Rakkiyappan. (2011). Delay-dependent stability of neutral systems with time-varying delays using delay-decomposition approach. Applied Mathematical Modelling. 36(5). 2253–2261. 80 indexed citations
15.
Balasubramaniam, P., R. Krishnasamy, & R. Rakkiyappan. (2011). Delay-interval-dependent robust stability results for uncertain stochastic systems with Markovian jumping parameters. Nonlinear Analysis Hybrid Systems. 5(4). 681–691. 28 indexed citations
16.
Rakkiyappan, R., P. Balasubramaniam, & R. Krishnasamy. (2010). Delay dependent stability analysis of neutral systems with mixed time-varying delays and nonlinear perturbations. Journal of Computational and Applied Mathematics. 235(8). 2147–2156. 43 indexed citations
17.
Balasubramaniam, P., R. Rakkiyappan, & R. Krishnasamy. (2010). Stochastic stability of Markovian jumping uncertain stochastic genetic regulatory networks with interval time-varying delays. Mathematical Biosciences. 226(2). 97–108. 37 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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